Selecting network routes based on aggregating models that predict node routing performance

ABSTRACT

The technologies described herein are generally directed to selecting network routes based on aggregating models that can predict routing performance in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include communicating, to second routing equipment, a first model describing a delay predicted to be caused to a future communication by the future communication being transited via the first routing equipment. The method can further include receiving, from the second routing equipment, a current communication for transit via the first routing equipment to destination equipment, wherein the first routing equipment was selected by the second routing equipment based on the first model, and second models, other than the first model, describing respective predicted delays from other routing equipment other than the first routing and second routing equipment.

TECHNICAL FIELD

The subject application is related to different approaches to handlingcommunication in networked computer systems and, for example, toselecting network routes based on predicted performance.

BACKGROUND

As network transmission speeds continue to increase, delays caused byindividual nodes of a communication route have increased insignificance. Delays that were insignificant in comparison to pasttransmission speeds have significantly increased in importance withother parts of the network having much higher throughput. UnlikeBandwidth, which in some circumstances can be increased by adding morefiber/circuits, delays associated with routing nodes can be limited bythe physical capacity of the routing nodes, e.g., processing speed ofrouter interfaces generally cannot be easily increased to mitigateproblematic delays.

These problems can become even more significant with the increase in thenumber of possible routes available for communication. In somecircumstances, by the time routing conditions of route nodes arereceived at upstream nodes selecting routes, the conditions havechanged, and are no longer as useful for selecting routes.

BRIEF DESCRIPTION OF THE DRAWINGS

The technology described herein is illustrated by way of example and notlimited in the accompanying figures in which like reference numeralsindicate similar elements and in which:

FIG. 1 is an architecture diagram of an example system that canfacilitate selecting network routes based on aggregating models that canpredict routing performance, in accordance with one or more embodiments.

FIG. 2 is a diagram of a non-limiting example system that can facilitateselecting network routes based on aggregating models that can predictrouting performance, in accordance with one or more embodiments.

FIG. 3 is a diagram of a non-limiting example system that can facilitateaggregating models that can predict node routing performance, inaccordance with one or more embodiments.

FIG. 4 depicts an example collection of interconnected nodes that canhave some route selections performed by one or more embodimentsdescribed herein.

FIG. 5 is a diagram of a non-limiting example system that can facilitateselecting network routes based on predictive models generated andmaintained by employing artificial intelligence/machine learning (AI/ML)approaches, in accordance with one or more embodiments.

FIG. 6 illustrates an example method that can facilitate selectingnetwork routes based on aggregating models that can predict routingperformance, in accordance with one or more embodiments.

FIG. 7 depicts a system that can facilitate selecting network routesbased on aggregating models that can predict routing performance, inaccordance with one or more embodiments.

FIG. 8 depicts an example non-transitory machine-readable medium thatcan include executable instructions that, when executed by a processorof a system, facilitate selecting network routes based on aggregatingmodels that can predict routing performance, in accordance with one ormore embodiments described above.

FIG. 9 illustrates an example block diagram of an example mobile handsetoperable to engage in a system architecture that can facilitateprocesses described herein, in accordance with one or more embodiments.

FIG. 10 illustrates an example block diagram of an example computeroperable to engage in a system architecture that can facilitateprocesses described herein, in accordance with one or more embodiments.

DETAILED DESCRIPTION

Generally speaking, one or more embodiments can facilitate selectingnetwork routes based on aggregating models that can predict routingperformance. In addition, one or more embodiments described herein canbe directed towards a multi-connectivity framework that supports theoperation of new radio (NR, sometimes referred to as 5G). As will beunderstood, one or more embodiments can allow an integration of userdevices with network assistance, by supporting control and mobilityfunctionality on cellular links (e.g., long term evolution (LTE) or NR).One or more embodiments can provide benefits including, systemrobustness, reduced overhead, and global resource management, whilefacilitating direct communication links via a NR sidelink.

It should be understood that any of the examples and terms used hereinare non-limiting. For instance, while examples are generally directed tonon-standalone operation where the NR backhaul links are operating onmillimeter wave (mmWave) bands and the control plane links are operatingon sub-6 GHz LTE bands, it should be understood that it isstraightforward to extend the technology described herein to scenariosin which the sub-6 GHz anchor carrier providing control planefunctionality could also be based on NR. As such, any of the examplesherein are non-limiting examples, any of the embodiments, aspects,concepts, structures, functionalities or examples described herein arenon-limiting, and the technology may be used in various ways thatprovide benefits and advantages in radio communications in general.

In some embodiments the non-limiting terms “signal propagationequipment” or simply “propagation equipment,” “radio network node” orsimply “network node,” “radio network device,” “network device,” andaccess elements are used herein. These terms may be usedinterchangeably, and refer to any type of network node that can serveuser equipment and/or be connected to other network node or networkelement or any radio node from where user equipment can receive asignal. Examples of radio network node include, but are not limited to,base stations (BS), multi-standard radio (MSR) nodes such as MSR BS,gNodeB, eNode B, network controllers, radio network controllers (RNC),base station controllers (BSC), relay, donor node controlling relay,base transceiver stations (BTS), access points (AP), transmissionpoints, transmission nodes, remote radio units (RRU) (also termed radiounits herein), remote ratio heads (RRH), and nodes in distributedantenna system (DAS). Additional types of nodes are also discussed withembodiments below, e.g., donor node equipment and relay node equipment,an example use of these being in a network with an integrated accessbackhaul network topology.

In some embodiments, the non-limiting term user equipment (UE) is used.This term can refer to any type of wireless device that can communicatewith a radio network node in a cellular or mobile communication system.Examples of UEs include, but are not limited to, a target device, deviceto device (D2D) user equipment, machine type user equipment, userequipment capable of machine to machine (M2M) communication, PDAs,tablets, mobile terminals, smart phones, laptop embedded equipped (LEE),laptop mounted equipment (LME), USB dongles, and other equipment thatcan have similar connectivity. Example UEs are described further withFIGS. 9 and 10 below. Some embodiments are described in particular for5G new radio systems. The embodiments are however applicable to anyradio access technology (RAT) or multi-RAT system where the UEs operateusing multiple carriers, e.g., LTE.

The computer processing systems, computer-implemented methods, apparatusand/or computer program products described herein employ hardware and/orsoftware to solve problems that are highly technical in nature (e.g.,processing multiple predictive models of network performance), that arenot abstract and cannot be performed as a set of mental acts by a human.For example, a human, or even a plurality of humans, cannot efficientlyand quickly analyze the relevant data with the same level of accuracyand/or efficiency as the various embodiments described herein.

Aspects of the subject disclosure will now be described more fullyhereinafter with reference to the accompanying drawings in which examplecomponents, graphs and selected operations are shown. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding of the variousembodiments. For example, some embodiments described can facilitateselecting network routes based on aggregating models that can predictrouting performance. Different examples that describe these aspects areincluded with the description of FIGS. 1-10 below. It should be notedthat the subject disclosure may be embodied in many different forms andshould not be construed as limited to this example or other examples setforth herein.

FIG. 1 is an architecture diagram of an example system 100 that canfacilitate selecting network routes based on aggregating models that canpredict routing performance, in accordance with one or more embodiments.For purposes of brevity, description of like elements and/or processesemployed in other embodiments is omitted. It should be noted that,although many examples herein discuss user equipment with one additionalnetwork identifier (e.g., dual-provisioned user equipment), one havingskill in the relevant art(s), given the description herein wouldappreciate that the approaches can also apply to any number of networkidentifiers associated with a user equipment.

As depicted, system 100 can include first routing equipment 150communicatively coupled via routes 126A-B to second routing equipment170 and third routing equipment 185 via network 190, respectively. Asdepicted, routing equipment can receive predictive models 172A-B fromsecond routing equipment 170 and third routing equipment 185,respectively. It should be noted that, as discussed herein, firstrouting equipment 150 can also be termed a node, router or device,without deviating from the spirit of embodiments described herein.Further, because whether first routing equipment 150 receivescommunications (e.g., transmission control protocol/internet protocol(TCP/IP) packets to be relayed to a destination node) from another‘upstream’ routing device, or is the originator of a communication,first routing equipment 150 can be termed herein as a source node, e.g.,as the node that is currently determining to which ‘downstream’ routingdevice to relay the communication.

As depicted, first routing equipment 150 can include computer executablecomponents 120, processor 160, storage device 162, and memory 165.Computer executable components 120 can include route identifyingcomponent 122, model receiving component 124, route selecting component126, and other components described or suggested by differentembodiments described herein, that can improve the operation of system100.

Further to the above, it should be appreciated that these components, aswell as aspects of the embodiments of the subject disclosure depicted inthis figure and various figures disclosed herein, are for illustrationonly, and as such, the architecture of such embodiments are not limitedto the systems, devices, and/or components depicted therein. Forexample, in some embodiments, first routing equipment 150 can furthercomprise various computer and/or computing-based elements describedherein with reference to mobile handset 900 of FIG. 9 , and operatingenvironment 1000 of FIG. 10 . For example, one or more of the differentfunctions of network equipment can be divided among various equipment,including, but not limited to, including equipment at a central nodeglobal control located on the core Network, e.g., mobile edge computing(MEC), self-organized networks (SON), or RAN intelligent controller(RIC) network equipment.

In some embodiments, memory 165 can comprise volatile memory (e.g.,random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), etc.)and/or non-volatile memory (e.g., read only memory (ROM), programmableROM (PROM), electrically programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), etc.) that can employ one or more memoryarchitectures. Further examples of memory 165 are described below withreference to system memory 1006 and FIG. 10 . Such examples of memory165 can be employed to implement any embodiments of the subjectdisclosure.

According to multiple embodiments, storage device 162 can include, butis not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, solid state drive (SSD) or other solid-state storagetechnology, Compact Disk Read Only Memory (CD ROM), digital video disk(DVD), blu-ray disk, or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computer.

According to multiple embodiments, processor 160 can comprise one ormore processors and/or electronic circuitry that can implement one ormore computer and/or machine readable, writable, and/or executablecomponents and/or instructions that can be stored on memory 165. Forexample, processor 160 can perform various operations that can bespecified by such computer and/or machine readable, writable, and/orexecutable components and/or instructions including, but not limited to,logic, control, input/output (I/O), arithmetic, and/or the like. In someembodiments, processor 160 can comprise one or more componentsincluding, but not limited to, a central processing unit, a multi-coreprocessor, a microprocessor, dual microprocessors, a microcontroller, asystem on a chip (SOC), an array processor, a vector processor, andother types of processors. Further examples of processor 160 aredescribed below with reference to processing unit 1004 of FIG. 10 . Suchexamples of processor 160 can be employed to implement any embodimentsof the subject disclosure.

In one or more embodiments, computer executable components 120 can beused in connection with implementing one or more of the systems,devices, components, and/or computer-implemented operations shown anddescribed in connection with FIG. 1 or other figures disclosed herein.For example, in one or more embodiments, computer executable components120 can include instructions that, when executed by processor 160, canfacilitate performance of operations defining route identifyingcomponent 122. As discussed with FIGS. 2-4 below, route identifyingcomponent 122 can, in accordance with one or more embodiments, identifya first route on a network from the source node equipment to destinationnode equipment via first node equipment and a second route on thenetwork from the source node equipment to the destination node equipmentvia second node equipment. For example, one or more embodiments of routeidentifying component 122 can identify routes 126A-B via network 190from first routing equipment 150 to second routing equipment 170 andthird routing equipment 185 via network 190, respectively. Additionaldetails regarding route selection by route identifying component 122 areprovided with the discussion of FIGS. 2 and 4 below.

Further, in another example, in one or more embodiments, computerexecutable components 120 can include instructions that, when executedby processor 160, can facilitate performance of operations definingmodel receiving component 124. As discussed with FIGS. 3-4 below, modelreceiving component 124 can, in accordance with one or more embodiments,receive from other node equipment, predictive models of respectivedelays predicted for different available routes. For example, in one ormore embodiments, receive, by the first routing equipment 150, from thesecond routing equipment 170 and the third routing equipment 185respectively, a first predictive model 172A and a second predictivemodel 172B of respective delays predicted for the first route and secondroute. One having skill in the relevant art(s), given the descriptionherein, appreciates that different approaches can be used to implementthe predictive models, including various machine learning approachesdescribed with FIG. 5 below.

Additional details regarding the generation of predictive models andreceipt and use by model receiving component 124 are provided with thediscussion of FIGS. 2 and 4 below.

In yet another example, computer executable components 120 can includeinstructions that, when executed by processor 160, can facilitateperformance of operations defining route selecting component 126. Asdiscussed herein, route selecting component 126 can, based on the firstpredictive model 172A and the second predictive model 172B, select aroute from a group of routes, e.g., based on predictive model 172A,route selecting component 126 can select route 126A from the group ofroutes 126A-B.

FIG. 2 is a diagram of a non-limiting example system 200 that canfacilitate selecting network routes based on aggregating models that canpredict routing performance, in accordance with one or more embodiments.For purposes of brevity, description of like elements and/or processesemployed in other embodiments is omitted.

As depicted, system 200 can include second routing equipment 170connected to first routing equipment 150 and fourth routing equipment280 via route 126A and route 226 via network 190. Second routingequipment 170 can include memory 165 that can store one or more computerand/or machine readable, writable, and/or executable components and/orinstructions 220 that, when respectively executed by processor 160, canfacilitate performance of operations defined by the executablecomponent(s) and/or instruction(s).

Generally, applications (e.g., computer executable components 220) caninclude routines, programs, components, data structures, etc., thatperform particular tasks or implement particular abstract data types. Insystem 200, computer executable components 220 can include networkcondition estimating component 212, receiving component 214,communicating component 216, and other components described or suggestedby different embodiments described herein that can improve the operationof system 200. It should be appreciated that these components, as wellas aspects of the embodiments of the subject disclosure depicted in thisfigure and various figures disclosed herein, are for illustration only,and as such, the architecture of such embodiments are not limited to thesystems, devices, and/or components depicted therein. For example, insome embodiments, one or more of the routing equipment discussed hereincan further comprise various computer and/or computing-based elementsdescribed herein with reference to mobile handset 900 of FIG. 9 andoperating environment 1000 described with FIG. 10 .

For example, in one or more embodiments, computer executable components220 can be used in connection with implementing one or more of thesystems, devices, components, and/or computer-implemented operationsshown and described in connection with FIG. 2 or other figures disclosedherein. For example, in one or more embodiments, computer executablecomponents 220 can include instructions that, when executed by processor160, can facilitate performance of operations defining network conditionestimating component 212. As discussed with FIGS. 4-5 below, in one ormore embodiments, network condition estimating component 212 can,communicate, to first routing equipment, a first model describing adelay predicted to be caused to a future communication by the futurecommunication being transited via the second routing equipment. Forexample, in one or more embodiments, network condition estimatingcomponent 212 can communicate predictive model 172A to first routingequipment 150, with this predictive model 172A describing the networkconditions (e.g., delay) predicted to be caused to a futurecommunication by the future communication being transited via the secondrouting equipment 170.

In another example, in one or more embodiments, computer executablecomponents 220 can include instructions that, when executed by processor160, can facilitate performance of operations defining, receivingcomponent 214. As discussed with FIGS. 4-5 below, receiving component214 can, in accordance with one or more embodiments, receive, from thefirst routing equipment, a current communication for transit via secondrouting equipment 170 to destination equipment (e.g., fourth routingequipment 280), with second routing equipment 170 being selected byfirst routing equipment 150 based on the first model, and the secondmodel, other than the first model, describing respective predicteddelays from other routing equipment other than the first routing andsecond routing equipment 185.

In another example, in one or more embodiments, computer executablecomponents 220 can include instructions that, when executed by processor160, can facilitate performance of operations defining, communicatingcomponent 216. As discussed with FIGS. 4-5 below, communicatingcomponent 216 can, in accordance with one or more embodiments, based onpredictive model 172A, can relay the current communication to secondrouting equipment 170 to transit the current communication todestination equipment, e.g., equipment corresponding to the destinationaddress of a packet forwarded via TCP/IP protocol to a next selectednetwork segment (router, node).

FIGS. 3 and 4 are diagrams that illustrate different approaches that canbe employed by one or more embodiments to route communications,including routing with aggregated predictive models described herein.For purposes of brevity, description of like elements and/or processesemployed in other embodiments is omitted.

As depicted in FIG. 3 , system 300 can include first routing equipment150 receiving predictive models 172A-B, e.g., from second routingequipment 170 and third routing equipment 185, respectively. In thisdepiction of first routing equipment 150, computer executable components120 further included model aggregating component 310 communicativelycoupled to storage device 162. Storage device 162 is depicted in anon-limiting manner as an example component to store aggregatedpredictive model 315, e.g., generated and maintained by modelaggregating component 310.

FIG. 4 depicts an example 400 collection of interconnected nodes thatcan have some route selections performed by one or more embodimentsdescribed herein. Example 400 includes first routing component 150,second routing equipment 170, third routing equipment 185, fourthrouting equipment 280, routing equipment 410, and destination routingequipment 420. First routing component 150 is communicatively coupled tostorage device 162, which stores aggregated predictive model 315 beingcomposed of an aggregate of predictive models 172A-B.

In an alternate embodiment to some of the embodiments above, as depictedin FIGS. 3 and 4 , when first routing equipment 150 is selecting betweenroutes 460A and 460D to second routing equipment 170 and third routingequipment 185 respectively, in addition to utilizing predictive models172A-B, first routing equipment 150 can use model aggregating component310 to predictive models 172A-B into aggregated predictive model 315stored, for example, in storage device 162. One having skill in therelevant art(s), given the description herein, appreciates thatdifferent approaches can be used to aggregate predictive models 172A-B,including approaches discussed below with FIG. 5 that utilize neuralnetworks.

It should be noted that, in the network shown in FIG. 4 , some or all ofthe routing equipment depicted can use a component similar to modelaggregating component 310 to aggregate predictive models from connectednoted. In some implementations, these aggregated models can be passed toother connected nodes, where they are further distributed. Thus, in anexample depicted destination routing equipment 420 can generate apredictive model of network conditions (e.g., based on models

In one approach, first routing equipment 150 can generate a localpredictive model of available network routes, e.g., using a componentsimilar to network condition estimating component 212 described withFIG. 2 above. This generated predictive model can be provided usingconnections 460C and 460E to routing equipment 410 and third routingequipment 185, respectively, where this model can be aggregated with thepresent predictive model at this equipment. By this process, theaggregated predictive models from second routing equipment 170 and thirdrouting equipment 185 can reach first routing equipment 150, where theresulting aggregated predictive model 315 can predict conditions in boththe 460A, 460B, 460C, and 460F route and the 460D, 460E route.

In additional embodiment, after a predictive model (e.g., aggregatedpredictive model 315) is used to select a route (e.g., route 460),routing components along the route can provide feedback 440A-B to therouting equipment that selected the route (e.g., first routing equipment150). This feedback can be used to incrementally update aspects ofdifferent models, e.g., to increase the accuracy of predictionsprovided.

FIG. 5 is a diagram of a non-limiting example system 500 that canfacilitate selecting network routes based on predictive models generatedand maintained by employing AI/ML approaches, in accordance with one ormore embodiments. For purposes of brevity, description of like elementsand/or processes employed in other embodiments is omitted.

As depicted, system 500 can comprise AI/ML components 510 to generateand maintaining aggregated predictive model 315 to facilitate selectingnetwork routes as described herein. In one or more embodiments, AI/MLcomponents 510 can comprise an artificial neural network (ANN), e.g.,initially trained and subsequently updated by predictive models receivedfrom other routers, and feedback 440A-B provided in response to previousroute selections. Example inputs that can be used to train AI/MLcomponents 510 can include historical network node performance, andfeedback 440A-B from specific route selections.

In certain embodiments, different functions of AI/ML component 510 canbe facilitated based on principles of AI that include, but are notlimited to, classifications, correlations, inferences and/orexpressions, with for example, AI/ML component 510 employing approachesthat include, but are not limited to, expert systems, fuzzy logic, statevector machines (SVMs), Hidden Markov Models (HMMs), greedy searchalgorithms, rule-based systems, Bayesian models (e.g., Bayesiannetworks), non-linear training techniques, data fusion, andutility-based analytical systems. Additional implementations can includeensemble ML algorithms/methods, including deep neural networks (DNN),reinforcement learning (RL), and long short-term memory (LSTM) networks.

FIG. 6 illustrates an example method 600 that can facilitate selectingnetwork routes based on aggregating models that can predict routingperformance, in accordance with one or more embodiments. For purposes ofbrevity, description of like elements and/or processes employed in otherembodiments is omitted.

At 602, method 600 can include identifying, by source node equipmentcomprising a processor, a first route on a network from the source nodeequipment to destination node equipment via first node equipment and asecond route on the network from the source node equipment to thedestination node equipment via second node equipment. For example, inone or more embodiments, route identifying component 122 can identify afirst route 126A on a network 190 from the source node equipment todestination node equipment via first node equipment and a second route126B on the network from the source node equipment to the destinationnode equipment via second node equipment.

At 604, method 600 can include, receiving, by the source node equipment,from the first and the second node equipment respectively, a firstpredictive model and a second predictive model of respective delayspredicted for the first route and second route. For example, in one ormore embodiments, model receiving component 124 can receive from thefirst and the second node equipment respectively, a first predictivemodel 172A and a second predictive model 172B of respective delayspredicted for the first route 126A and second route 126B.

At 606, method 600 can include, based on the first predictive model andthe second predictive model, employing route selecting component 126 toselect, by the source node equipment, a route from a group of routes,comprising the first route and the second route, for communication ofinformation to the destination node equipment.

FIG. 7 depicts a system 700 that can facilitate selecting network routesbased on aggregating models that can predict routing performance, inaccordance with one or more embodiments. For purposes of brevity,description of like elements and/or processes employed in otherembodiments is omitted. As depicted, system 700 can include routeidentifying component 122, model receiving component 124, routeselecting component 126, and other components described or suggested bydifferent embodiments described herein, that can improve the operationof system 700.

In an example, component 702 can include the functions of routeidentifying component 122, supported by the other layers of system 700.For example, in an embodiment, component 702 can identify a first routeon a network from the source node equipment to destination nodeequipment via first node equipment and a second route on the networkfrom the source node equipment to the destination node equipment viasecond node equipment

In this and other examples, component 704 can include the functions ofmodel receiving component 124, supported by the other layers of system700. Continuing this example, in one or more embodiments, component 704can receive, from the first and the second node equipment respectively,a first predictive model and a second predictive model of respectivedelays predicted for the first route and second route.

In an additional example, component 706 can include the functions ofroute selecting component 126, supported by the other layers of system700. For example, component 706 can employ route selecting component 126to select, by the source node equipment, a route from a group of routes,comprising the first route and the second route, for communication ofinformation to the destination node equipment

FIG. 8 depicts an example 800 non-transitory machine-readable medium 810that can include executable instructions that, when executed by aprocessor of a system, facilitate selecting network routes based onaggregating models that can predict routing performance, in accordancewith one or more embodiments described above. For purposes of brevity,description of like elements and/or processes employed in otherembodiments is omitted. As depicted, non-transitory machine-readablemedium 810 includes executable instructions that can facilitateperformance of operations 802-808.

In one or more embodiments, the operations can include operation 802that can include identifying, by source node equipment comprising aprocessor, a first route on a network from the source node equipment todestination node equipment via first node equipment and a second routeon the network from the source node equipment to the destination nodeequipment via second node equipment.

Operations can further include operation 804, that can receive from thefirst and the second node equipment respectively, a first predictivemodel and a second predictive model of respective delays predicted forthe first route and second route. In one or more embodiments, theoperations can further include operation 806 that can, based on thefirst predictive model and the second predictive model, select, by thesource node equipment, a route from a group of routes, comprising thefirst route and the second route, for communication of information tothe destination node equipment from the source node equipment, resultingin a selected route.

FIG. 9 illustrates an example block diagram of an example mobile handset900 operable to engage in a system architecture that facilitateswireless communications according to one or more embodiments describedherein. Although a mobile handset is illustrated herein, it will beunderstood that other devices can be a mobile device, and that themobile handset is merely illustrated to provide context for theembodiments of the various embodiments described herein. The followingdiscussion is intended to provide a brief, general description of anexample of a suitable environment in which the various embodiments canbe implemented. While the description includes a general context ofcomputer-executable instructions embodied on a machine-readable storagemedium, those skilled in the art will recognize that the embodimentsalso can be implemented in combination with other program modules and/oras a combination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules, orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, solid statedrive (SSD) or other solid-state storage technology, Compact Disk ReadOnly Memory (CD ROM), digital video disk (DVD), Blu-ray disk, or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bythe computer. In this regard, the terms “tangible” or “non-transitory”herein as applied to storage, memory or computer-readable media, are tobe understood to exclude only propagating transitory signals per se asmodifiers and do not relinquish rights to all standard storage, memoryor computer-readable media that are not only propagating transitorysignals per se.

Communication media typically embodies computer-readable instructions,data structures, program modules, or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media

The handset includes a processor 902 for controlling and processing allonboard operations and functions. A memory 904 interfaces to theprocessor 902 for storage of data and one or more applications 906(e.g., a video player software, user feedback component software, etc.).Other applications can include voice recognition of predetermined voicecommands that facilitate initiation of the user feedback signals. Theapplications 906 can be stored in the memory 904 and/or in a firmware908, and executed by the processor 902 from either or both the memory904 or/and the firmware 908. The firmware 908 can also store startupcode for execution in initializing the handset 900. A communicationscomponent 910 interfaces to the processor 902 to facilitatewired/wireless communication with external systems, e.g., cellularnetworks, VoIP networks, and so on. Here, the communications component910 can also include a suitable cellular transceiver 911 (e.g., a GSMtransceiver) and/or an unlicensed transceiver 913 (e.g., Wi-Fi, WiMax)for corresponding signal communications. The handset 900 can be a devicesuch as a cellular telephone, a PDA with mobile communicationscapabilities, and messaging-centric devices. The communicationscomponent 910 also facilitates communications reception from terrestrialradio networks (e.g., broadcast), digital satellite radio networks, andInternet-based radio services networks

The handset 900 includes a display 912 for displaying text, images,video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 912 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 912 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface914 is provided in communication with the processor 902 to facilitatewired and/or wireless serial communications (e.g., USB, and/or IEEE1294) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This supports updating andtroubleshooting the handset 900, for example. Audio capabilities areprovided with an audio I/O component 916, which can include a speakerfor the output of audio signals related to, for example, indication thatthe user pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 916 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The handset 900 can include a slot interface 918 for accommodating a SIC(Subscriber Identity Component) in the form factor of a card SIM oruniversal SIM 920, and interfacing the SIM card 920 with the processor902. However, it is to be appreciated that the SIM card 920 can bemanufactured into the handset 900, and updated by downloading data andsoftware.

The handset 900 can process IP data traffic through the communicationscomponent 910 to accommodate IP traffic from an IP network such as, forexample, the Internet, a corporate intranet, a home network, a personarea network, etc., through an ISP or broadband cable provider. Thus,VoIP traffic can be utilized by the handset 900 and IP-based multimediacontent can be received in either an encoded or a decoded format.

A video processing component 922 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 922can aid in facilitating the generation, editing, and sharing of videoquotes. The handset 900 also includes a power source 924 in the form ofbatteries and/or an AC power subsystem, which power source 924 caninterface to an external power system or charging equipment (not shown)by a power I/O component 926.

The handset 900 can also include a video component 930 for processingvideo content received and, for recording and transmitting videocontent. For example, the video component 930 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 932 facilitates geographically locating the handset 900. Asdescribed hereinabove, this can occur when the user initiates thefeedback signal automatically or manually. A user input component 934facilitates the user initiating the quality feedback signal. The userinput component 934 can also facilitate the generation, editing andsharing of video quotes. The user input component 934 can include suchconventional input device technologies such as a keypad, keyboard,mouse, stylus pen, and/or touch screen, for example.

Referring again to the applications 906, a hysteresis component 936facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 938 can be provided that facilitatestriggering of the hysteresis component 936 when the Wi-Fi transceiver913 detects the beacon of the access point. A SIP client 940 enables thehandset 900 to support SIP protocols and register the subscriber withthe SIP registrar server. The applications 906 can also include a client942 that provides at least the capability of discovery, play and storeof multimedia content, for example, music.

The handset 900, as indicated above related to the communicationscomponent 910, includes an indoor network radio transceiver 913 (e.g.,Wi-Fi transceiver). This function supports the indoor radio link, suchas IEEE 802.11, for the dual-mode GSM handset 900. The handset 900 canaccommodate at least satellite radio services through a handset that cancombine wireless voice and digital radio chipsets into a single handhelddevice.

Network 190 can employ various cellular systems, technologies, andmodulation schemes to facilitate wireless radio communications betweendevices. While example embodiments include use of 5G new radio (NR)systems, one or more embodiments discussed herein can be applicable toany radio access technology (RAT) or multi-RAT system, including whereuser equipment operate using multiple carriers, e.g., LTE FDD/TDD,GSM/GERAN, CDMA2000, etc. For example, wireless communication system 200can operate in accordance with global system for mobile communications(GSM), universal mobile telecommunications service (UMTS), long termevolution (LTE), LTE frequency division duplexing (LTE FDD, LTE timedivision duplexing (TDD), high speed packet access (HSPA), code divisionmultiple access (CDMA), wideband CDMA (WCMDA), CDMA2000, time divisionmultiple access (TDMA), frequency division multiple access (FDMA),multi-carrier code division multiple access (MC-CDMA), single-carriercode division multiple access (SC-CDMA), single-carrier FDMA (SC-FDMA),orthogonal frequency division multiplexing (OFDM), discrete Fouriertransform spread OFDM (DFT-spread OFDM) single carrier FDMA (SC-FDMA),Filter bank based multi-carrier (FBMC), zero tail DFT-spread-OFDM (ZTDFT-s-OFDM), generalized frequency division multiplexing (GFDM), fixedmobile convergence (FMC), universal fixed mobile convergence (UFMC),unique word OFDM (UW-OFDM), unique word DFT-spread OFDM (UWDFT-Spread-OFDM), cyclic prefix OFDM CP-OFDM, resource-block-filteredOFDM, Wi Fi, WLAN, WiMax, and the like. However, various features andfunctionalities of system 100 are particularly described wherein thedevices of system 100 are configured to communicate wireless signalsusing one or more multi carrier modulation schemes, wherein data symbolscan be transmitted simultaneously over multiple frequency subcarriers(e.g., OFDM, CP-OFDM, DFT-spread OFMD, UFMC, FMBC, etc.). Theembodiments are applicable to single carrier as well as to multicarrier(MC) or carrier aggregation (CA) operation of the user equipment. Theterm carrier aggregation (CA) is also called (e.g., interchangeablycalled) “multi-carrier system”, “multi-cell operation”, “multi-carrieroperation”, “multi-carrier” transmission and/or reception. Note thatsome embodiments are also applicable for Multi RAB (radio bearers) onsome carriers (that is data plus speech is simultaneously scheduled).

Various embodiments described herein can be configured to provide andemploy 5G wireless networking features and functionalities. With 5Gnetworks that may use waveforms that split the bandwidth into severalsub bands, different types of services can be accommodated in differentsub bands with the most suitable waveform and numerology, leading toimproved spectrum utilization for 5G networks. Notwithstanding, in themmWave spectrum, the millimeter waves have shorter wavelengths relativeto other communications waves, whereby mmWave signals can experiencesevere path loss, penetration loss, and fading. However, the shorterwavelength at mmWave frequencies also allows more antennas to be packedin the same physical dimension, which allows for large-scale spatialmultiplexing and highly directional beamforming.

FIG. 10 provides additional context for various embodiments describedherein, intended to provide a brief, general description of a suitableoperating environment 1000 in which the various embodiments of theembodiment described herein can be implemented. While the embodimentshave been described above in the general context of computer-executableinstructions that can run on one or more computers, those skilled in theart will recognize that the embodiments can be also implemented incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the various methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, Internet of Things (IoT)devices, distributed computing systems, as well as personal computers,hand-held computing devices, microprocessor-based or programmableconsumer electronics, and the like, each of which can be operativelycoupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage media,and/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media or machine-readablestorage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable ormachine-readable instructions, program modules, structured data orunstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD), Blu-ray disc (BD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, solid state drives or other solid statestorage devices, or other tangible and/or non-transitory media which canbe used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 10 , the example operating environment 1000for implementing various embodiments of the aspects described hereinincludes a computer 1002, the computer 1002 including a processing unit1004, a system memory 1006 and a system bus 1008. The system bus 1008couples system components including, but not limited to, the systemmemory 1006 to the processing unit 1004. The processing unit 1004 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1004.

The system bus 1008 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1006includes ROM 1010 and RAM 1012. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1002, such as during startup. The RAM 1012 can also include a high-speedRAM such as static RAM for caching data.

The computer 1002 further includes an internal hard disk drive (HDD)1014 (e.g., EIDE, SATA), one or more external storage devices 1016(e.g., a magnetic floppy disk drive (FDD) 1016, a memory stick or flashdrive reader, a memory card reader, etc.) and a drive 1020, e.g., suchas a solid-state drive, an optical disk drive, which can read or writefrom a disk 1022, such as a CD-ROM disc, a DVD, a BD, etc.Alternatively, where a solid-state drive is involved, disk 1022 wouldnot be included, unless separate. While the internal HDD 1014 isillustrated as located within the computer 1002, the internal HDD 1014can also be configured for external use in a suitable chassis (notshown). Additionally, while not shown in environment 1000, a solid-statedrive (SSD) could be used in addition to, or in place of, an HDD 1014.The HDD 1014, external storage device(s) 1016 and drive 1020 can beconnected to the system bus 1008 by an HDD interface 1024, an externalstorage interface 1026 and a drive interface 1028, respectively. Theinterface 1024 for external drive implementations can include at leastone or both of Universal Serial Bus (USB) and Institute of Electricaland Electronics Engineers (IEEE) 1394 interface technologies. Otherexternal drive connection technologies are within contemplation of theembodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1002, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to respective types of storage devices, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, whether presently existing ordeveloped in the future, could also be used in the example operatingenvironment, and further, that any such storage media can containcomputer-executable instructions for performing the methods describedherein.

A number of program modules can be stored in the drives and RAM 1012,including an operating system 1030, one or more application programs1032, other program modules 1034 and program data 1036. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1012. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

Computer 1002 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 1030, and the emulatedhardware can optionally be different from the hardware illustrated inFIG. 10 . In such an embodiment, operating system 1030 can comprise onevirtual machine (VM) of multiple VMs hosted at computer 1002.Furthermore, operating system 1030 can provide runtime environments,such as the Java runtime environment or the .NET framework, forapplications 1032. Runtime environments are consistent executionenvironments that allow applications 1032 to run on any operating systemthat includes the runtime environment. Similarly, operating system 1030can support containers, and applications 1032 can be in the form ofcontainers, which are lightweight, standalone, executable packages ofsoftware that include, e.g., code, runtime, system tools, systemlibraries and settings for an application.

Further, computer 1002 can be enable with a security module, such as atrusted processing module (TPM). For instance, with a TPM, bootcomponents hash next in time boot components, and wait for a match ofresults to secured values, before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 1002, e.g., applied at the application execution level or atthe operating system (OS) kernel level, thereby enabling security at anylevel of code execution.

A user can enter commands and information into the computer 1002 throughone or more wired/wireless input devices, e.g., a keyboard 1038, a touchscreen 1040, and a pointing device, such as a mouse 1042. Other inputdevices (not shown) can include a microphone, an infrared (IR) remotecontrol, a radio frequency (RF) remote control, or other remote control,a joystick, a virtual reality controller and/or virtual reality headset,a game pad, a stylus pen, an image input device, e.g., camera(s), agesture sensor input device, a vision movement sensor input device, anemotion or facial detection device, a biometric input device, e.g.,fingerprint or iris scanner, or the like. These and other input devicesare often connected to the processing unit 1004 through an input deviceinterface 1044 that can be coupled to the system bus 1008, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, a BLUETOOTH®interface, etc.

A monitor 1046 or other type of display device can be also connected tothe system bus 1008 via an interface, such as a video adapter 1048. Inaddition to the monitor 1046, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1002 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1050. The remotecomputer(s) 1050 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1002, although, for purposes of brevity, only a memory/storage device1052 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1054 and/orlarger networks, e.g., a wide area network (WAN) 1056. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1002 can beconnected to the local network 1054 through a wired and/or wirelesscommunication network interface or adapter 1058. The adapter 1058 canfacilitate wired or wireless communication to the LAN 1054, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 1058 in a wireless mode.

When used in a WAN networking environment, the computer 1002 can includea modem 1060 or can be connected to a communications server on the WAN1056 via other means for establishing communications over the WAN 1056,such as by way of the Internet. The modem 1060, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 1008 via the input device interface 1044. In a networkedenvironment, program modules depicted relative to the computer 1002 orportions thereof, can be stored in the remote memory/storage device1052. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

When used in either a LAN or WAN networking environment, the computer1002 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 1016 asdescribed above, such as but not limited to a network virtual machineproviding one or more aspects of storage or processing of information.Generally, a connection between the computer 1002 and a cloud storagesystem can be established over a LAN 1054 or WAN 1056 e.g., by theadapter 1058 or modem 1060, respectively. Upon connecting the computer1002 to an associated cloud storage system, the external storageinterface 1026 can, with the aid of the adapter 1058 and/or modem 1060,manage storage provided by the cloud storage system as it would othertypes of external storage. For instance, the external storage interface1026 can be configured to provide access to cloud storage sources as ifthose sources were physically connected to the computer 1002.

The computer 1002 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, store shelf, etc.), and telephone. This can include WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

Further to the description above, as it employed in the subjectspecification, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components, or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor mayalso be implemented as a combination of computing processing units.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can include both volatile andnonvolatile memory.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media, device readablestorage devices, or machine-readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software or firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can include a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Additionally, the terms “core-network”, “core”, “core carrier network”,“carrier-side”, or similar terms can refer to components of atelecommunications network that typically provides some or all ofaggregation, authentication, call control and switching, charging,service invocation, or gateways. Aggregation can refer to the highestlevel of aggregation in a service provider network wherein the nextlevel in the hierarchy under the core nodes is the distribution networksand then the edge networks. User equipment do not normally connectdirectly to the core networks of a large service provider, but can berouted to the core by way of a switch or radio area network.Authentication can refer to determinations regarding whether the userrequesting a service from the telecom network is authorized to do sowithin this network or not. Call control and switching can referdeterminations related to the future course of a call stream acrosscarrier equipment based on the call signal processing. Charging can berelated to the collation and processing of charging data generated byvarious network nodes. Two common types of charging mechanisms found inpresent day networks can be prepaid charging and postpaid charging.Service invocation can occur based on some explicit action (e.g., calltransfer) or implicitly (e.g., call waiting). It is to be noted thatservice “execution” may or may not be a core network functionality asthird-party network/nodes may take part in actual service execution. Agateway can be present in the core network to access other networks.Gateway functionality can be dependent on the type of the interface withanother network.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components (e.g., supportedthrough artificial intelligence, as through a capacity to makeinferences based on complex mathematical formalisms), that can providesimulated vision, sound recognition and so forth.

Aspects, features, or advantages of the subject matter can be exploitedin substantially any, or any, wired, broadcast, wirelesstelecommunication, radio technology or network, or combinations thereof.Non-limiting examples of such technologies or networks include Geocasttechnology; broadcast technologies (e.g., sub-Hz, ELF, VLF, LF, MF, HF,VHF, UHF, SHF, THz broadcasts, etc.); Ethernet; X.25; powerline-typenetworking (e.g., PowerLine AV Ethernet, etc.); femto-cell technology;Wi-Fi; Worldwide Interoperability for Microwave Access (WiMAX); EnhancedGeneral Packet Radio Service (Enhanced GPRS); Third GenerationPartnership Project (3GPP or 3G) Long Term Evolution (LTE); 3GPPUniversal Mobile Telecommunications System (UMTS) or 3GPP UMTS; ThirdGeneration Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB);High Speed Packet Access (HSPA); High Speed Downlink Packet Access(HSDPA); High Speed Uplink Packet Access (HSUPA); GSM Enhanced DataRates for GSM Evolution (EDGE) Radio Access Network (RAN) or GERAN;Terrestrial Radio Access Network (UTRAN); or LTE Advanced.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methods herein.One of ordinary skill in the art may recognize that many furthercombinations and permutations of the disclosure are possible.Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

While the various embodiments are susceptible to various modificationsand alternative constructions, certain illustrated implementationsthereof are shown in the drawings and have been described above indetail. It should be understood, however, that there is no intention tolimit the various embodiments to the specific forms disclosed, but onthe contrary, the intention is to cover all modifications, alternativeconstructions, and equivalents falling within the spirit and scope ofthe various embodiments.

In addition to the various implementations described herein, it is to beunderstood that other similar implementations can be used, ormodifications and additions can be made to the describedimplementation(s) for performing the same or equivalent function of thecorresponding implementation(s) without deviating therefrom. Stillfurther, multiple processing chips or multiple devices can share theperformance of one or more functions described herein, and similarly,storage can be affected across a plurality of devices. Accordingly, theembodiments are not to be limited to any single implementation, butrather are to be construed in breadth, spirit and scope in accordancewith the appended claims.

What is claimed is:
 1. A method, comprising: identifying, by source nodeequipment comprising a processor, a first route on a network from thesource node equipment to destination node equipment via first nodeequipment and a second route on the network from the source nodeequipment to the destination node equipment via second node equipment;receiving, by the source node equipment, from the first and the secondnode equipment respectively, a first predictive model and a secondpredictive model of respective delays predicted for the first route andsecond route; and based on the first predictive model and the secondpredictive model, selecting, by the source node equipment, a route froma group of routes, comprising the first route and the second route, forcommunication of information to the destination node equipment from thesource node equipment, resulting in a selected route.
 2. The method ofclaim 1, further comprising, based on the first predictive model and thesecond predictive model, generating, by the source node equipment, athird predictive model of a delay predicted for the communication of theinformation to the destination node equipment via the source nodeequipment.
 3. The method of claim 2, wherein selecting the route isfurther based on the third predictive model.
 4. The method of claim 2,wherein the third predictive model comprises a neural network trainedwith data describing the network.
 5. The method of claim 4, furthercomprising: receiving, by the source node equipment, from node equipmentimplicated by communication using the selected route, route informationcorresponding to a result of the communication of the information viathe selected route; and updating, by the source node equipment, thethird predictive model based on the route information.
 6. The method ofclaim 5, wherein updating the third predictive model comprises furthertraining the neural network based on the route information.
 7. Themethod of claim 1, wherein the first predictive model was generated bythe first node equipment based on route information collected by thefirst node equipment.
 8. The method of claim 7, wherein the firstpredictive model was generated by the first node equipment further basedon the route information describing routes similar to the first route.9. The method of claim 7, wherein the route information collected by thefirst node equipment comprises historical information describing pastperformance communicating via the first route.
 10. The method of claim1, wherein the first predictive model comprises information describing athird route, between the first node equipment and the destination nodeequipment.
 11. First routing equipment, comprising: a processor; and amemory that stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising:communicating, to second routing equipment, a first model describing adelay predicted to be caused to a future communication by the futurecommunication being transited via the first routing equipment,receiving, from the second routing equipment, a current communicationfor transit via the first routing equipment to destination equipment,wherein the first routing equipment was selected by the second routingequipment based on the first model, and second models, other than thefirst model, describing respective predicted delays from other routingequipment other than the first routing and second routing equipment, andrelaying the current communication to third routing equipment to transitthe current communication to the destination equipment.
 12. The firstrouting equipment of claim 11, wherein the operations further comprise,updating the first model based on a current delay caused to the currentcommunication by transit via the first routing equipment.
 13. The firstrouting equipment of claim 11, wherein the first routing equipment wasselected by the second routing equipment based on a first neural networktrained based on the first model, and the second models.
 14. The firstrouting equipment of claim 13, wherein the operations further comprisecommunicating information corresponding to the current delay to thesecond routing equipment for further training of the first neuralnetwork.
 15. The first routing equipment of claim 13, wherein the firstmodel is generated based on a second neural network, and wherein theoperations further comprise: receiving model information correspondingto the first neural network, and based on the routing information,updating the second neural network.
 16. The first routing equipment ofclaim 11, wherein the first model was generated by the first routingequipment based on routing information collected by the first routingequipment.
 17. A machine-readable storage medium, comprising executableinstructions that, when executed by a processor of a first routingdevice, facilitate performance of operations, the operations comprising:identifying a first routing network path from the first routing deviceto a destination device via a first network router and a second routingnetwork path from the first routing device to the destination device viaa second network router; receiving, from the first network router andthe second network router, respectively, a first predictive model of afirst delay predicted for the first routing network path and a secondpredictive model of a second delay predicted for the second routingnetwork path; and based on the first predictive model and the secondpredictive model, selecting the first routing network path forcommunication of a transmission control protocol packet to thedestination device from the first routing device, resulting in aselected network path.
 18. The machine-readable storage medium of claim17, wherein the operations further comprise, based on the firstpredictive model and the second predictive model, identifying a thirdpredictive model of a third delay predicted for communication ofinformation to the destination device via the first routing device. 19.The machine-readable storage medium of claim 18, wherein selecting thefirst routing network path is further based on the third predictivemodel.
 20. The machine-readable storage medium of claim 18, wherein thethird predictive model comprises a model output from a neural networktrained with data describing a network comprising the first routingnetwork path and the second routing network path.